Shiny Dashboards

07 September 2020

Introduction
Example of Shiny Apps
Deployment

Introduction

About Me

BSc (Mathematical Sciences)
MSc (Statistics)
PhD (Computational Statistics)
- Prof. Nial Friel
PostDoc
- Insight Centre for Data Analytics (UCD)
- Prof. Andrew Parnell
Data Scientist (Clavis Insight)
- Ecommerce analytics startup
- Acquired by Ascential 2017
Senior Data Scientist (Edge by Ascential)
- Combination of 4 companies

My Journey with Shiny

First created a Shiny App in 2015
- Teaching app to help explain regression and correlation

My Journey with Shiny

First created a Shiny App in 2015
- Teaching app to help explain regression and correlation
Second App created during Postdoc
- Needed a way of allowing a non technical user to run my code

My Journey with Shiny

First created a Shiny App in 2015
- Teaching app to help explain regression and correlation
Second App created during Postdoc
- Needed a way of allowing a non technical user to run my code
Current Role
- 6 Shiny apps in use internally
- Various uses
  - Run ML algorithms
  - ETL workflows
  - Database access

My Journey with Shiny

First created a Shiny App in 2015
- Teaching app to help explain regression and correlation
Second App created during Postdoc
- Needed a way of allowing a non technical user to run my code
Current Role
- 6 Shiny apps in use internally
- Various uses
  - Run ML algorithms
  - ETL workflows
  - Database access
Personal Projects
- Exploring and visualising data

Dashboards

Examples

Football Data
- github.com/aboland/footballR
- Premier League Data for last 20 years
- Various Statistics
Dublin Bus
- github.com/aboland/DublinRTPI
- Uses Public API to get real time bus/train info
- Collate info for multiple stops and routes

Plotly

Graphing library
- R, Python, JavaScript
Has integration with ggplot2 and shiny
Plots can be also be saved as HTML files or displayed within R Markdown HTML documents or presentations

library(ggplot2)
my_plot <- 
  ggplot(mpg, aes(x = displ, y = hwy, colour = class)) +
  geom_point()
my_plot

library(plotly)
my_plot <- 
  ggplot(mpg, aes(x = displ, y = hwy, colour = class)) +
  geom_point()
ggplotly(my_plot)

library(plotly)
my_plot <- 
  ggplot(mpg, aes(x = displ, y = hwy, colour = class)) +
  geom_point() + theme(plot.background = element_rect(fill = "transparent", color = NA))
ggplotly(my_plot)

app.R

library(shiny)
library(plotly)

ui <- fluidPage(
    plotlyOutput("sample_plot")
)

server <- function(input, output) {
    output$sample_plot <- renderPlotly({
        my_plot <- 
            ggplot(mpg, aes(displ, hwy, colour = class)) +
            geom_point()
        ggplotly(my_plot)
    })
}

# Run the application 
shinyApp(ui = ui, server = server)

Plotly - Save as HTML

library(htmlwidgets)
saveWidget(ggplotly(my_plot), "~/Desktop/sample_plotly.html")

Dublin RTPI

Real Time Passenger Information
Entry for RStudio’s Shiny Contest 2019
Obtains data from public API
Displays data for multiple bus stops

URL Header Variables

...
query <- parseQueryString(session$clientData$url_search)

if ("routes" %in% names(query)) {
  selected_stops <- unlist(strsplit(query$stops, ","))
} else{
  selected_stops <- c(334, 336)  # Use as default
}
...

Deployment

shinyapps.io

Free
- 5 applications
- 12 active hours
Starter $100/yr $\approx$ £75
- 25 applications
- 100 active hours
Basic $440/yr $\approx$ £333
- Unlimited applications
- 500 active hours

Pros
- Simple to deploy app
- Almost no maintenance
- Easy to start and stop apps
- Visibility on app usage
Cons
- Non customisable URL’s
  (without pro plan)
- App limits
- Usage limits

Amazon Web Services

Server cost
- $94.26
  (EC2 t3.micro + 8GB storage)
- €33.76 aboland.ie domain
- Total €114 $\approx$ £102

Pros
- Flexibility
- Custom URL’s
- Can use server for other uses e.g. website, storage, API’s
Cons
- Harder to deploy apps
- Server maintenance
  - AWS management
  - Machine libraries
- Harder to get visibility on app usage

Thank you

github.com/aboland/RSS2020
- presentation.Rmd